Fault diagnosis of rotating machinery under time-varying speed based on order tracking and deep learning

Due to the disadvantages that rely on prior knowledge and expert experience in traditional order analysis methods and deep learning cannot accurately extract the features in time-varying conditions. A fault diagnosis method for rotating machinery under time-varying conditions based on tacholess order tracking (TOT) and deep learning is proposed in this paper. Firstly, frequency domain periodic signals and estimated speed information are obtained by order tracking. Secondly, the frequency domain periodic signal is speed normalized using the estimated speed information. Finally, normalized features are extracted by deep learning network to form feature vector. The feature vector is fed into a softmax layer to complete fault diagnosis of the gearbox. The fault diagnosis of the gearbox results are compared with other traditional methods and show that the proposed fault diagnosis method can effectively identify the faults and obtain higher fault diagnosis accuracy under time-varying speed.